Sentiment Analysis on Tabungan Perumahan Rakyat (TAPERA) Program by using Support Vector Machine (SVM)
DOI:
https://doi.org/10.30871/jaic.v8i2.8694Keywords:
Sentiment Analysis, Support Vector Machine (SVM), TaperaAbstract
This study aims to analyze public sentiment towards the Housing Savings Program (TAPERA) using the Support Vector Machine (SVM) algorithm. The dataset comprises 16,061 reviews about TAPERA which was gathered from web scrapping and YouTube API. The sentiment analysis results indicate that 99.8% of the reviews are negative, while only 0.2% are positive. The SVM model applied in this study achieved a very high accuracy rate of 99.81%. This indicates that the model is highly effective in classifying sentiments, particularly in identifying negative sentiments. The resulting confusion matrix shows the model's excellent performance in detecting negative sentiments, with no False Positives (FP) and a very high number of True Negatives (TN). However, the model exhibits weaknesses in detecting positive sentiments, as indicated by the presence of several False Negatives (FN) and the absence of True Positives (TP). The findings of this study suggest that the public generally holds a very negative view of the TAPERA program. This insight is crucial for program administrators to consider as they evaluate and improve the program based on negative feedback received from the public. Overall, this research provides important insights into public perceptions of TAPERA and underscores the need for better modeling for more representative sentiment analysis. These findings can serve as a basis for policymakers in designing more effective communication strategies and program improvements to increase public acceptance of TAPERA.
Downloads
References
H. G. Putra, E. Fahmi, and K. Taruc, 'Tabungan Perumahan Rakyat (Tapera) dan Penerapannya di DKI Jakarta', Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan, vol. 3, no. 2, p. 321, Jan. 2020, doi: 10.24912/jmstkik.v3i2.5630.
N. Tania, J. Novienco, and dan Dixon Sanjaya, 'Kajian Hukum Progresif Terhadap Implementasi Produk Tabungan Perumahan Rakyat', Kajian Masalah Hukum dan Pembangunan, 2021, [Online]. Available: www.cnnindonesia.com/nasional/20190903212554-20-427289/
F. Aftab et al., 'A Comprehensive Survey on Sentiment Analysis Techniques', International Journal of Technology, vol. 14, no. 6, pp. 1288"“1298, 2023, doi: 10.14716/IJTECH.V14I6.6632.
M. Wankhade, A. C. S. Rao, and C. Kulkarni, 'A survey on sentiment analysis methods, applications, and challenges', Artificial Intelligence Review 2022 55:7, vol. 55, no. 7, pp. 5731"“5780, Feb. 2022, doi: 10.1007/S10462-022-10144-1.
P. Gonçalves, M. Araújo, F. Benevenuto, and M. Cha, 'Comparing and combining sentiment analysis methods', COSN 2013 - Proceedings of the 2013 Conference on Online Social Networks, pp. 27"“37, 2013, doi: 10.1145/2512938.2512951.
M. F. Fakhrezi, A. F. Rochim, D. Mutiara, and K. Nugraheni, 'Comparison of Sentiment Analysis Methods Based on Accuracy Value Case Study: Twitter Mentions of Academic Article', Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 7, no. 1, pp. 161"“167, Feb. 2023, doi: 10.29207/RESTI.V7I1.4767.
R. N. Handayani, 'Optimasi Algoritma Support Vector Machine untuk Analisis Sentimen pada Ulasan Produk Tokopedia Menggunakan PSO', Media Informatika, vol. 20, no. 2, pp. 97"“108, Jul. 2021, doi: 10.37595/MEDIAINFO.V20I2.59.
A. Shmilovici, 'Support Vector Machines', Data Mining and Knowledge Discovery Handbook, pp. 257"“276, 2005, doi: 10.1007/0-387-25465-X_12.
R. Burbidge and B. Buxton, 'An introduction to support vector machines for data mining', 2001.
S. Huang, C. A. I. Nianguang, P. Penzuti Pacheco, S. Narandes, Y. Wang, and X. U. Wayne, 'Applications of Support Vector Machine (SVM) Learning in Cancer Genomics', Cancer Genomics Proteomics, vol. 15, no. 1, p. 41, Jan. 2018, doi: 10.21873/CGP.20063.
N. H. Ovirianti, M. Zarlis, and H. Mawengkang, 'Support Vector Machine Using A Classification Algorithm', Sinkron : jurnal dan penelitian teknik informatika, vol. 6, no. 3, pp. 2103"“2107, Aug. 2022, doi: 10.33395/SINKRON.V7I3.11597.
Z. Zhang, Z. Liu, and C. Qiao, 'Tendency Mining in Dynamic Association Rules Based on SVM Classifier', 2014.
A. Mat Deris, A. Mohd Zain, and R. Sallehuddin, 'Overview of Support Vector Machine in Modeling Machining Performances', Procedia Eng, vol. 24, pp. 308"“312, Jan. 2011, doi: 10.1016/J.PROENG.2011.11.2647.
W. Chu, S. S. Keerthi, and C. J. Ong, 'A general formulation for support vector machines', ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age, vol. 5, pp. 2522"“2526, 2002, doi: 10.1109/ICONIP.2002.1201949.
O. Devos, G. Downey, and L. Duponchel, 'Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils', Food Chem, vol. 148, pp. 124"“130, 2014, doi: 10.1016/J.FOODCHEM.2013.10.020.
Y. Qi and Z. Shabrina, 'Sentiment analysis using Twitter data: a comparative application of lexicon- and machine-learning-based approach', Soc Netw Anal Min, vol. 13, no. 1, Dec. 2023, doi: 10.1007/S13278-023-01030-X.
P. Kurniawati, R. Y. Fa'rifah, and D. Witarsyah, 'Sentiment Analysis of Maxim Online Transportation App Reviews using Support Vector Machine (SVM) Algorithm', Building of Informatics, Technology and Science (BITS), vol. 5, no. 2, Sep. 2023, doi: 10.47065/bits.v5i2.4265.
M. P. Dwi Cahyo, Widodo, and B. Prasetya Adhi, 'Kinerja Algoritma Support Vector Machine dalam MenentukanKebenaran Informasi Banjir di Twitter', PINTER : Jurnal Pendidikan Teknik Informatika dan Komputer, vol. 3, no. 2, pp. 116"“121, Dec. 2019, doi: 10.21009/pinter.3.2.5.
J. Friadi, and D. E. Kurniawan, "Analisis Sentimen Ulasan Wisatawan Terhadap Alun-Alun Kota Batam: Perbandingan Kinerja Metode Naive Bayes dan Support Vector Machine," Jurnal Sistem Informasi Bisnis, vol. 14, no. 4, pp. 403-407, Oct. 2024. https://doi.org/10.21456/vol14iss4pp403-407
D. Vonega, A. Fadila, and D. Kurniawan, "Analisis Sentimen Twitter Terhadap Opini Publik Atas Isu Pencalonan Puan Maharani dalam PILPRES 2024", JAIC, vol. 6, no. 2, pp. 129-135, Nov. 2022.
M. Narvekar and S. F. Syed, 'An optimized algorithm for association rule mining using FP tree', in Procedia Computer Science, Elsevier B.V., 2015, pp. 101"“110. doi: 10.1016/j.procs.2015.03.097.
F. O. Widarta and R. A. Syahputra, 'The application of machine learning algorithms for assessing the maturity level of palm fruits as the prominent commodity in the Western-Southern Area of Aceh', Operations Excellence: Journal of Applied Industrial Engineering, vol. 16, no. 1, pp. 56"“63, Jun. 2024, doi: 10.22441/OE.2024.V16.I1.102.
D. D. Soeprapto, 'SWOT analysis of BP. Tapera: A public housing savings implementing agency in Indonesia', International Journal of Research in Business and Social Science (2147- 4478), vol. 9, no. 6, pp. 230"“243, Oct. 2020, doi: 10.20525/IJRBS.V9I6.900.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Rizki Agam Syahputra, Riski Arifin, Suriadi Suriadi, Muhammad Iqbal
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
